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Course Code

appliedml

Duration

14
hours (usually 2 days including breaks)

Overview

This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.

Course Outline

Naive Bayes

Multinomial models

Bayesian categorical data analysis

Discriminant analysis

Linear regression

Logistic regression

GLM

EM Algorithm

Mixed Models

Additive Models

Classification

KNN

Bayesian Graphical Models

Factor Analysis (FA)

Principal Component Analysis (PCA)

Independent Component Analysis (ICA)

Support Vector Machines (SVM) for regression and classification

Boosting

Ensemble models

Neural networks

Hidden Markov Models (HMM)

Space State Models

Clustering

Public Classroom

Participants from multiple organisations. Topics usually cannot be customised

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